Databricks Data Analyst Associate Practice Test

A data analyst opens a table result in a Databricks notebook and wants to automatically see summary statistics for all column types — including distributions, value counts, null percentages, and histograms — without writing any SQL. Which built-in Databricks feature provides this automatically?

1. ARunning SELECT * FROM table LIMIT 100 — returns a sample of raw row data but provides no automatic summary statistics, distribution charts, or null percentage indicators for any column type
2. BThe data profile feature in a notebook result cell — automatically computes and displays summary statistics (min, max, mean, standard deviation, null count) for numeric columns, value frequencies for string columns, and date range information for date columns, along with histograms per column, without requiring any SQL to be written
3. CRunning DESCRIBE table_name — returns metadata including column names, data types, and catalog comments registered on the table, but does not compute or display runtime statistical distributions or data quality information for column values
4. DRunning SHOW CREATE TABLE table_name — outputs the DDL statement that was used to define the table schema, storage format, and table properties, but contains no runtime statistical information about the data stored in the table
5. ERunning EXPLAIN EXTENDED on a SELECT query — returns the Spark physical and logical plan with operator-level statistics estimates used by the query optimizer, but does not provide user-accessible per-column descriptive statistics or visualization histograms

A data analyst runs a Spark SQL query in a Databricks notebook using spark.sql() and assigns the result to a variable called df. Which built-in Databricks function renders an interactive results grid with the ability to add chart visualizations directly beneath the notebook cell?

1. Aprint(df) — Python's built-in print function renders a truncated text table that can be converted to a chart using a right-click context menu available in the cell output area
2. Bdisplay(df) — a Databricks-specific function that renders an interactive results grid below the cell, with a '+' tab to add chart types such as bar, line, or scatter
3. Cdf.show() — calling this DataFrame method renders an HTML-formatted result set that includes a visualization toolbar for selecting chart types and column configurations
4. Ddf.plot(kind='bar') — calling this method invokes the built-in Databricks chart engine, which prompts the user for X-axis and Y-axis column selections before rendering the chart
5. Edf.render() — this standard Databricks notebook function transforms any Spark DataFrame into a rich, interactive HTML visualization widget with full chart configuration support

A data analyst's dashboard query is taking much longer than expected. She wants to visualize the directed acyclic graph (DAG) of query execution operators, identify the most time-consuming operator stages, and understand memory consumption at each step. Which Databricks tool provides this functionality?

1. AThe Query History list — displays the list of past query runs with their status, duration, and user, but does not provide a DAG visualization or operator-level timing and memory breakdown for diagnosing performance bottlenecks
2. BThe Query Profile — accessible from Query History, displays a DAG of operators with per-node metrics (time spent, rows processed, memory peak) and a Top operators panel that highlights the most expensive steps
3. CThe Catalog Explorer — provides lineage graphs of data assets showing which upstream tables feed into a given table or view, but does not show query execution plans or operator-level performance metrics for SQL queries
4. DEXPLAIN EXTENDED on the query — outputs the Spark optimizer's logical, analyzed, optimized, and physical plans as plain text, but does not provide runtime metrics, actual row counts, memory usage, or a visual DAG based on real execution data
5. EThe DESCRIBE HISTORY command — returns a transaction log of all write operations performed on a Delta table with their timestamps and metadata, but does not analyze or diagnose the runtime performance of SQL SELECT queries

A Genie space author wants to run a structured test to measure whether Genie correctly answers a predefined set of business questions. The author also wants to compare Genie's responses against expected SQL results for scoring. Which Genie feature is purpose-built for this use case?

1. AThe Monitoring tab — it allows authors to filter all historical user questions by status and rating and export them as a batch evaluation report scored against the space's most recent instruction version
2. BResponse feedback — collecting thumbs-up or thumbs-down ratings from business users on live production questions provides statistically reliable accuracy metrics for the space over time
3. CInspect mode — enabling inspect mode on all test questions causes Genie to self-evaluate its SQL results by running subsidiary validation statements and reporting an accuracy score for each tested query
4. DBenchmarks — authors define a set of test questions with optional expected SQL answers; when run, Genie's responses are scored against those expected results for accuracy without influencing Genie's instruction context
5. EGeneral instructions text block — authors can include a numbered list of expected correct SQL outputs in the space's general instructions, which Databricks uses as an automated regression test suite each time the space is saved

A data analyst is tasked with cleaning a Unity Catalog Delta table called bronze.sales.transactions. The table has the following data quality issues: (1) rows where transaction_amount (DOUBLE) is negative — these represent data entry errors and must be permanently removed, (2) rows where customer_id (STRING) is NULL — these must also be permanently removed, and (3) rows where country_code (STRING) contains values not in the approved list ('US', 'CA', 'MX', 'GB') — these must be updated to 'UNKNOWN' rather than deleted. Which SQL block CORRECTLY addresses all three requirements in a single transaction-efficient approach?

1. AStep 1: TRUNCATE TABLE bronze.sales.transactions; to remove all rows. Step 2: Re-insert valid rows using INSERT INTO bronze.sales.transactions SELECT * FROM bronze.sales.transactions_backup WHERE transaction_amount >= 0 AND customer_id IS NOT NULL AND country_code IN ('US','CA','MX','GB'); This approach is preferred because Delta Lake does not support row-level DELETE with compound predicates — only full-table TRUNCATE followed by selective re-insert is guaranteed to avoid write conflicts on partitioned Delta tables registered in Unity Catalog.
2. BCombine both deletion conditions into a single DELETE statement, and use a separate UPDATE for the country code correction: DELETE FROM bronze.sales.transactions WHERE transaction_amount < 0 OR customer_id IS NULL; followed by UPDATE bronze.sales.transactions SET country_code = 'UNKNOWN' WHERE country_code NOT IN ('US', 'CA', 'MX', 'GB'); This approach uses Delta Lake's native ACID DML to permanently remove invalid rows in one DELETE operation (combining both predicates with OR), then correct the invalid country codes with a targeted UPDATE — both operations commit independently as separate ACID transactions on the Delta table.
3. CRow-level DELETE is not supported on DOUBLE columns in Databricks SQL because floating-point comparison is non-deterministic under ACID semantics; only integer and string column predicates are supported in DELETE WHERE clauses. The analyst must first cast transaction_amount to BIGINT with ALTER TABLE bronze.sales.transactions ALTER COLUMN transaction_amount TYPE BIGINT, then run the DELETE. The NOT IN predicate is also unsupported in Delta Lake UPDATE statements and must be rewritten as a LEFT ANTI JOIN subquery in a MERGE statement.
4. DUse a single MERGE statement: MERGE INTO bronze.sales.transactions AS t USING (SELECT transaction_id FROM bronze.sales.transactions WHERE transaction_amount < 0 OR customer_id IS NULL) AS src ON t.transaction_id = src.transaction_id WHEN MATCHED THEN DELETE; followed by a separate UPDATE bronze.sales.transactions SET country_code = 'UNKNOWN' WHERE country_code NOT IN ('US','CA','MX','GB'); This is equivalent to option B but forces the entire operation into a single MERGE scan, which is always required when deleting rows from a Delta table that has more than 1 million rows — smaller tables can use plain DELETE.
5. EAll three cleaning operations can be combined into a single MERGE statement where the source dataset contains three logical branches: one for rows to delete due to negative transaction_amount, one for rows to delete due to NULL customer_id, and one for rows requiring a country_code update. MERGE is the only DML statement capable of mixing DELETE and UPDATE actions against the same target table in a single pass, making it the most efficient approach. Plain DELETE for row removal followed by a separate UPDATE is not supported when both operations target the same Delta table within the same Databricks session — Databricks SQL enforces a 'one DML per session transaction' rule for Delta tables registered in Unity Catalog.

A metastore admin grants the SELECT privilege on a catalog to a group of data analysts. According to Unity Catalog's inheritance model, which objects does this privilege automatically apply to?

1. AOnly the catalog object itself; child schemas each require a separate GRANT SELECT statement before users can query their tables
2. BThe catalog, plus its parent metastore, because privilege inheritance in Unity Catalog flows both downward to children and upward to the metastore
3. CAll schemas that existed at the time the GRANT was executed, but not schemas or tables that are created after the GRANT statement runs
4. DAll current and future schemas and nested objects within that catalog
5. EOnly managed tables registered under the catalog, since external tables require separate GRANT statements executed directly on each external location

A financial services company wants to enrich its internal customer transaction data with third-party macroeconomic indicators and pre-built ML models for risk scoring. A data architect recommends Databricks Marketplace to source these assets. Which statement MOST ACCURATELY describes the role and capabilities of Databricks Marketplace in this use case?

1. ADatabricks Marketplace is a cloud cost optimization service where Databricks customers negotiate discounted compute pricing for high-volume analytical workloads. While it provides access to Databricks-certified SQL Warehouse configurations, it does not facilitate the sharing or acquisition of third-party datasets, ML models, or any external data products — those functions are handled outside of Databricks through separate data vendor contracts.
2. BDatabricks Marketplace is a paid commercial app store exclusively for purchasing proprietary Databricks Runtime plugins, licensed cluster acceleration libraries, and custom serialization engines. Organizations procure Marketplace items entirely through their cloud provider's billing portal (AWS Marketplace, Azure Marketplace, or Google Cloud Marketplace), and any data-sharing between organizations is handled independently by Delta Sharing, which operates as a completely separate product from Databricks Marketplace.
3. CDatabricks Marketplace is an open forum for exchanging data products, powered by the Delta Sharing protocol, where data providers list datasets, Databricks notebooks, Solution Accelerators, ML models, and MCP servers. Data consumers can browse the public Open Marketplace at marketplace.databricks.com without a Databricks account, request access to listings from within their Unity Catalog-enabled workspace, and — once access is granted — query shared datasets directly as Unity Catalog objects alongside their own internal data.
4. DDatabricks Marketplace is a capability exclusive to Databricks Enterprise and Premium tier customers that enables internal teams within a single organization to securely exchange Delta tables across multiple workspaces located in different cloud regions. It uses Databricks' proprietary DataShare binary format and is intentionally not interoperable with external third-party providers, open data standards, or any non-Databricks cloud storage systems.
5. EDatabricks Marketplace is a machine learning model hub — analogous to HuggingFace Hub — where data scientists and AI engineers download pre-trained neural network weights and deploy them directly to Databricks Model Serving endpoints. Its scope is limited entirely to AI model distribution for inference workloads; it does not support tabular datasets, reference data, or any non-model data products, and all models available in Marketplace are validated and certified by Databricks before listing.